'프로그램 사용'에 해당되는 글 2263건

  1. 2021.01.09 CNN과 RNN
  2. 2021.01.09 darknet과 darknetab
  3. 2021.01.08 darknet openmp 빌드
  4. 2021.01.08 darknet on rpi3
  5. 2021.01.08 yolo lite
  6. 2021.01.08 SSDnnn (Single Shot Detector)
  7. 2021.01.07 CNN - YOLO
  8. 2021.01.07 MPI/PMI
  9. 2021.01.06 warewulf - wwsh 명령어
  10. 2021.01.05 rtl sdr driver libusb
프로그램 사용/yolo_tensorflow2021. 1. 9. 17:31

CNN과 RNN

RNN(Recurrent Neural Network)

 

CNN(Convolution Neural Network)

합성곱신경망, convolution과 pooling

 

[링크 : http://ebbnflow.tistory.com/119]

[링크 : http://dbrang.tistory.com/1537]

 

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darknet on rpi3  (0) 2021.01.08
Posted by 구차니
프로그램 사용/yolo_tensorflow2021. 1. 9. 16:46

darknet과 darknetab

원본 darknet은 성능에 영향을 줄게 3개 밖에 없는데

GPU=0
CUDNN=0
OPENCV=0
OPENMP=0

 

alexeyAB의 darknet은 gpu, cudnn, avx, openmp 4가지 이다.

GPU=0
CUDNN=0
CUDNN_HALF=0
OPENCV=0
AVX=0
OPENMP=0
LIBSO=0
ZED_CAMERA=0
ZED_CAMERA_v2_8=0

[링크 : https://github.com/AlexeyAB/darknet]

 

심심해서(?) i5-2세대도 있나 보는데 어라..? 있네?

[링크 : https://ark.intel.com/.../intel-core-i5-2500-processor-6m-cache-up-to-3-70-ghz.html]

[링크 : https://ark.intel.com/../intel-core-i5-2520m-processor-3m-cache-up-to-3-20-ghz.html]

 

근데 빌드해서 돌려보니 내꺼는 AVX일뿐이라 돌아가지 않는다 ㅠㅠ

AVX2는 하스웰 이후부터 지원한다고 하니.. 집에있는 내 실험용 컴퓨터로는 무리겠구나..

$ ./darknet detect cfg/yolov3.cfg ../yolov3.weights data/dog.jpg
 GPU isn't used 
 Used AVX 
 Not used FMA & AVX2 
 OpenCV isn't used - data augmentation will be slow 
명령어가 잘못됨 (core dumped)

 

alexeyAB 버전을 싱글 코어 / openmp 설정으로 돌리니 반정도 줄었다.

data/dog.jpg: Predicted in 11175.292000 milli-seconds.
data/dog.jpg: Predicted in 5974.575000 milli-seconds.
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yolo lite  (0) 2021.01.08
Posted by 구차니
프로그램 사용/yolo_tensorflow2021. 1. 8. 18:01

darknet openmp 빌드

위는 오리지널 darknet을 아무런 옵션없이 라즈베리에서 빌드한 결과

$ ldd darknet
        linux-vdso.so.1 (0x7efe1000)
        /usr/lib/arm-linux-gnueabihf/libarmmem-${PLATFORM}.so => /usr/lib/arm-linux-gnueabihf/libarmmem-v7l.so (0x76f54000)
        libm.so.6 => /lib/arm-linux-gnueabihf/libm.so.6 (0x76eb6000)
        libpthread.so.0 => /lib/arm-linux-gnueabihf/libpthread.so.0 (0x76e8c000)
        libc.so.6 => /lib/arm-linux-gnueabihf/libc.so.6 (0x76d3e000)
        /lib/ld-linux-armhf.so.3 (0x76f69000)

 

아래는 darknet AlexeyAB 버전을 neon과 openmp 설정해서 빌드한 결과

$ ldd darknet
        linux-vdso.so.1 (0x7eefc000)
        /usr/lib/arm-linux-gnueabihf/libarmmem-${PLATFORM}.so => /usr/lib/arm-linux-gnueabihf/libarmmem-v7l.so (0x76f79000)
        libgomp.so.1 => /lib/arm-linux-gnueabihf/libgomp.so.1 (0x76f25000)
        libstdc++.so.6 => /lib/arm-linux-gnueabihf/libstdc++.so.6 (0x76dde000)
        libm.so.6 => /lib/arm-linux-gnueabihf/libm.so.6 (0x76d5c000)
        libgcc_s.so.1 => /lib/arm-linux-gnueabihf/libgcc_s.so.1 (0x76d2f000)
        libpthread.so.0 => /lib/arm-linux-gnueabihf/libpthread.so.0 (0x76d05000)
        libc.so.6 => /lib/arm-linux-gnueabihf/libc.so.6 (0x76bb7000)
        libdl.so.2 => /lib/arm-linux-gnueabihf/libdl.so.2 (0x76ba4000)
        /lib/ld-linux-armhf.so.3 (0x76f8e000)

 

 

+

cpu only = 30.13sec

$ ./tiny.sh
 GPU isn't used
 OpenCV isn't used - data augmentation will be slow
mini_batch = 1, batch = 1, time_steps = 1, train = 0
   layer   filters  size/strd(dil)      input                output
   0 conv     16       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  16 0.150 BF
   1 max                2x 2/ 2    416 x 416 x  16 ->  208 x 208 x  16 0.003 BF
   2 conv     32       3 x 3/ 1    208 x 208 x  16 ->  208 x 208 x  32 0.399 BF
   3 max                2x 2/ 2    208 x 208 x  32 ->  104 x 104 x  32 0.001 BF
   4 conv     64       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  64 0.399 BF
   5 max                2x 2/ 2    104 x 104 x  64 ->   52 x  52 x  64 0.001 BF
   6 conv    128       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x 128 0.399 BF
   7 max                2x 2/ 2     52 x  52 x 128 ->   26 x  26 x 128 0.000 BF
   8 conv    256       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 256 0.399 BF
   9 max                2x 2/ 2     26 x  26 x 256 ->   13 x  13 x 256 0.000 BF
  10 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF
  11 max                2x 2/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.000 BF
  12 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  13 conv    256       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 256 0.089 BF
  14 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF
  15 conv    255       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 255 0.044 BF
  16 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  17 route  13                                     ->   13 x  13 x 256
  18 conv    128       1 x 1/ 1     13 x  13 x 256 ->   13 x  13 x 128 0.011 BF
  19 upsample                 2x    13 x  13 x 128 ->   26 x  26 x 128
  20 route  19 8                                   ->   26 x  26 x 384
  21 conv    256       3 x 3/ 1     26 x  26 x 384 ->   26 x  26 x 256 1.196 BF
  22 conv    255       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 255 0.088 BF
  23 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 5.571
avg_outputs = 341534
Loading weights from ../yolov3-tiny.weights...
 seen 64, trained: 32013 K-images (500 Kilo-batches_64)
Done! Loaded 24 layers from weights-file
 Detection layer: 16 - type = 28
 Detection layer: 23 - type = 28
data/dog.jpg: Predicted in 30133.750000 milli-seconds.
dog: 81%
bicycle: 38%
car: 71%
truck: 42%
truck: 62%
car: 40%
Not compiled with OpenCV, saving to predictions.png instead

 

neon = 10.718 sec

$ ./tiny.sh
 GPU isn't used
 OpenCV isn't used - data augmentation will be slow
mini_batch = 1, batch = 1, time_steps = 1, train = 0
   layer   filters  size/strd(dil)      input                output
   0 conv     16       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  16 0.150 BF
   1 max                2x 2/ 2    416 x 416 x  16 ->  208 x 208 x  16 0.003 BF
   2 conv     32       3 x 3/ 1    208 x 208 x  16 ->  208 x 208 x  32 0.399 BF
   3 max                2x 2/ 2    208 x 208 x  32 ->  104 x 104 x  32 0.001 BF
   4 conv     64       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  64 0.399 BF
   5 max                2x 2/ 2    104 x 104 x  64 ->   52 x  52 x  64 0.001 BF
   6 conv    128       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x 128 0.399 BF
   7 max                2x 2/ 2     52 x  52 x 128 ->   26 x  26 x 128 0.000 BF
   8 conv    256       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 256 0.399 BF
   9 max                2x 2/ 2     26 x  26 x 256 ->   13 x  13 x 256 0.000 BF
  10 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF
  11 max                2x 2/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.000 BF
  12 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  13 conv    256       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 256 0.089 BF
  14 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF
  15 conv    255       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 255 0.044 BF
  16 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  17 route  13                                     ->   13 x  13 x 256
  18 conv    128       1 x 1/ 1     13 x  13 x 256 ->   13 x  13 x 128 0.011 BF
  19 upsample                 2x    13 x  13 x 128 ->   26 x  26 x 128
  20 route  19 8                                   ->   26 x  26 x 384
  21 conv    256       3 x 3/ 1     26 x  26 x 384 ->   26 x  26 x 256 1.196 BF
  22 conv    255       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 255 0.088 BF
  23 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 5.571
avg_outputs = 341534
Loading weights from ../yolov3-tiny.weights...
 seen 64, trained: 32013 K-images (500 Kilo-batches_64)
Done! Loaded 24 layers from weights-file
 Detection layer: 16 - type = 28
 Detection layer: 23 - type = 28
data/dog.jpg: Predicted in 10718.416000 milli-seconds.
dog: 81%
bicycle: 38%
car: 71%
truck: 42%
truck: 62%
car: 40%
Not compiled with OpenCV, saving to predictions.png instead

 

openmp = 8.686 sec

$ ./tiny.sh
 GPU isn't used
 OpenCV isn't used - data augmentation will be slow
mini_batch = 1, batch = 1, time_steps = 1, train = 0
   layer   filters  size/strd(dil)      input                output
   0 conv     16       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  16 0.150 BF
   1 max                2x 2/ 2    416 x 416 x  16 ->  208 x 208 x  16 0.003 BF
   2 conv     32       3 x 3/ 1    208 x 208 x  16 ->  208 x 208 x  32 0.399 BF
   3 max                2x 2/ 2    208 x 208 x  32 ->  104 x 104 x  32 0.001 BF
   4 conv     64       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  64 0.399 BF
   5 max                2x 2/ 2    104 x 104 x  64 ->   52 x  52 x  64 0.001 BF
   6 conv    128       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x 128 0.399 BF
   7 max                2x 2/ 2     52 x  52 x 128 ->   26 x  26 x 128 0.000 BF
   8 conv    256       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 256 0.399 BF
   9 max                2x 2/ 2     26 x  26 x 256 ->   13 x  13 x 256 0.000 BF
  10 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF
  11 max                2x 2/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.000 BF
  12 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  13 conv    256       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 256 0.089 BF
  14 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF
  15 conv    255       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 255 0.044 BF
  16 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  17 route  13                                     ->   13 x  13 x 256
  18 conv    128       1 x 1/ 1     13 x  13 x 256 ->   13 x  13 x 128 0.011 BF
  19 upsample                 2x    13 x  13 x 128 ->   26 x  26 x 128
  20 route  19 8                                   ->   26 x  26 x 384
  21 conv    256       3 x 3/ 1     26 x  26 x 384 ->   26 x  26 x 256 1.196 BF
  22 conv    255       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 255 0.088 BF
  23 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 5.571
avg_outputs = 341534
Loading weights from ../yolov3-tiny.weights...
 seen 64, trained: 32013 K-images (500 Kilo-batches_64)
Done! Loaded 24 layers from weights-file
 Detection layer: 16 - type = 28
 Detection layer: 23 - type = 28
data/dog.jpg: Predicted in 8686.237000 milli-seconds.
dog: 81%
bicycle: 38%
car: 71%
truck: 42%
truck: 62%
car: 40%
Not compiled with OpenCV, saving to predictions.png instead

 

 

openmp + neon = 4.449 sec

$ ./tiny.sh
 GPU isn't used
 OpenCV isn't used - data augmentation will be slow
mini_batch = 1, batch = 1, time_steps = 1, train = 0
   layer   filters  size/strd(dil)      input                output
   0 conv     16       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  16 0.150 BF
   1 max                2x 2/ 2    416 x 416 x  16 ->  208 x 208 x  16 0.003 BF
   2 conv     32       3 x 3/ 1    208 x 208 x  16 ->  208 x 208 x  32 0.399 BF
   3 max                2x 2/ 2    208 x 208 x  32 ->  104 x 104 x  32 0.001 BF
   4 conv     64       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  64 0.399 BF
   5 max                2x 2/ 2    104 x 104 x  64 ->   52 x  52 x  64 0.001 BF
   6 conv    128       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x 128 0.399 BF
   7 max                2x 2/ 2     52 x  52 x 128 ->   26 x  26 x 128 0.000 BF
   8 conv    256       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 256 0.399 BF
   9 max                2x 2/ 2     26 x  26 x 256 ->   13 x  13 x 256 0.000 BF
  10 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF
  11 max                2x 2/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.000 BF
  12 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  13 conv    256       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 256 0.089 BF
  14 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF
  15 conv    255       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 255 0.044 BF
  16 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  17 route  13                                     ->   13 x  13 x 256
  18 conv    128       1 x 1/ 1     13 x  13 x 256 ->   13 x  13 x 128 0.011 BF
  19 upsample                 2x    13 x  13 x 128 ->   26 x  26 x 128
  20 route  19 8                                   ->   26 x  26 x 384
  21 conv    256       3 x 3/ 1     26 x  26 x 384 ->   26 x  26 x 256 1.196 BF
  22 conv    255       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 255 0.088 BF
  23 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 5.571
avg_outputs = 341534
Loading weights from ../yolov3-tiny.weights...
 seen 64, trained: 32013 K-images (500 Kilo-batches_64)
Done! Loaded 24 layers from weights-file
 Detection layer: 16 - type = 28
 Detection layer: 23 - type = 28
data/dog.jpg: Predicted in 4449.888000 milli-seconds.
dog: 81%
bicycle: 38%
car: 71%
truck: 42%
truck: 62%
car: 40%
Not compiled with OpenCV, saving to predictions.png instead

 

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프로그램 사용/yolo_tensorflow2021. 1. 8. 16:39

darknet on rpi3

원인 불명으로 BUS error나 Segmentation fault가 떠서 찾아보니

소스를 수정한 버전이 있다고 한다.

돌아는 가는데.... (이하 생략)

 

[링크 : https://j-remind.tistory.com/53]

[링크 : https://github.com/AlexeyAB/darknet]

 

설마 endian 문제?

[링크 : https://github.com/pjreddie/darknet/issues/823]

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프로그램 사용/yolo_tensorflow2021. 1. 8. 16:05

yolo lite

YOLOv2-tiny 의 web implementation 이라는데 웹 implementation이 머지?

 

[링크 : https://reu2018dl.github.io/]

[링크 : https://github.com/reu2018DL/YOLO-LITE]

[링크 : https://arxiv.org/pdf/1811.05588.pdf]

 

 

+

MS COCO 2014 and PASCAL VOC 2007 + 2012

 

pascal voc(Visual Object Classes)

[링크 : http://host.robots.ox.ac.uk/pascal/VOC/]

[링크 : https://ndb796.tistory.com/500]

 

MS COCO

COCO랑은 다른가?

[링크 : https://arxiv.org/pdf/1405.0312.pdf]

[링크 : https://chacha95.github.io/2020-02-27-Object-Detection4/]

[링크 : https://cocodataset.org/#home]

 

AP - Average Precision

mAP (mean AP)

[링크 : https://mezzaninex.tistory.com/entry/AI-COCO-Dataset-mAPmean-Average-Precision]

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프로그램 사용/yolo_tensorflow2021. 1. 8. 16:01

SSDnnn (Single Shot Detector)

SSD 도 모델이군..

 

[링크 : https://pjreddie.com/darknet/yolo/]

[링크 : https://junjiwon1031.github.io/2017/09/08/Single-Shot-Multibox-Detector.html]

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프로그램 사용/yolo_tensorflow2021. 1. 7. 17:19

CNN - YOLO

 

[링크 : https://medium.com/curg/you-only-look-once-다-단지-한-번만-보았을-뿐이라구-bddc8e6238e2]

[링크 : https://curt-park.github.io/2017-03-26/yolo/]

 

[링크 : https://pjreddie.com/darknet/yolo/]

[링크 : https://zeuseyera.github.io/darknet-kr/3_ImageNet_BunRyu/BunRyu.html]

 

*net?

AlexNet

[링크 : https://en.wikipedia.org/wiki/AlexNet]

 

VGGNet 이라는 CNN모델

[링크 : https://89douner.tistory.com/61]

 

LeNet, ZFNet, ...

[링크 : https://blog.naver.com/laonple/221218707503]

 

inception/googlenet

[링크 : https://ikkison.tistory.com/86]

[링크 : https://kangbk0120.github.io/articles/2018-01/inception-googlenet-review]

 

 

net으로 끝나서 먼가 했는데 일단은 이미지 데이터베이스, 단어 데이터베이스 인 듯.

단지 데이터베이스이기 때문에 딥 러닝에 용이해서 언급이 되는 건가?

 

ImageNet is an image database organized according to the WordNet hierarchy

[링크 : http://www.image-net.org/] imagenet

 

WordNet® is a large lexical database of English.

[링크 : https://wordnet.princeton.edu/] wordnet

 

 

COCO - Common Object in COntext

[링크 : https://cocodataset.org/#home]

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프로그램 사용/openHPC2021. 1. 7. 16:13

MPI/PMI

회문인가?

찾는데 정말 안나오네 ㅠㅠ

 

[링크 : https://slurm.schedmd.com/mpi_guide.html]

 

Process Management Interface Exascale (PMIx) 

[링크 : https://openpmix.github.io/]

 

These process managers communicate with MPICH processes using a predefined interface called as PMI (process management interface).

[링크 : https://wiki.mpich.org/mpich/index.php/Frequently_Asked_Questions]

 

+

slurm.schedmd.com/mpiplugins.html

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프로그램 사용/openHPC2021. 1. 6. 17:13

warewulf - wwsh 명령어

openhpc 문서에서 하라는대로만 따라해서

어떤 원리로 어떻게 관리되는지 알지 못해서 구조를 좀 파악해야할 필요가 발생 ㅠㅠ

 

[링크 : https://github.com/nicksan2c/wwsh]

[링크 : https://dokuwiki.wesleyan.edu/doku.php?id=cluster:144]

 

+

> file /usr/bin/ww*
/usr/bin/wwan:        symbolic link to bluetooth
/usr/bin/wwbootstrap: Perl script text executable
/usr/bin/wwconfig:    Perl script text executable
/usr/bin/wwinit:      Bourne-Again shell script, ASCII text executable
/usr/bin/wwlivesync:  Perl script text executable
/usr/bin/wwmkchroot:  Bourne-Again shell script, ASCII text executable
/usr/bin/wwnodescan:  Perl script text executable
/usr/bin/wwsh:        Perl script text executable
/usr/bin/wwuseradd:   POSIX shell script, ASCII text executable
/usr/bin/wwvnfs:      Perl script text executable

 

wwsh list 모음

> wwsh bootstrap list
BOOTSTRAP NAME            SIZE (M)      ARCH
5.3.18-lp152.57-default   58.0          x86_64

> wwsh ipmi list
NAME                AUTO    IPMI_IPADDR    IPMI_NETMASK
================================================================================
openhpc-1           no      UNDEF          UNDEF
openhpc-2           no      UNDEF          UNDEF

> wwsh node list
NAME                GROUPS              IPADDR              HWADDR
================================================================================
openhpc-1           UNDEF               10.0.2.4            08:00:27:4b:df:4f
openhpc-2           UNDEF               10.0.2.5            08:00:27:f9:ca:3f

> wwsh object list
NAME                       _TYPE
======================================================
passwd                     file
group                      file
shadow                     file
munge.key                  file
5.3.18-lp152.57-default    bootstrap
leap15.2                   vnfs
network                    file
openhpc-1                  node
dynamic_hosts              file
openhpc-2                  node

> wwsh provision list
NODE                VNFS            BOOTSTRAP             FILES
================================================================================
openhpc-1           leap15.2        5.3.18-lp152.57-de... dynamic_hosts,grou...
openhpc-2           leap15.2        5.3.18-lp152.57-de... dynamic_hosts,grou...

> wwsh vnfs list
VNFS NAME            SIZE (M)   ARCH       CHROOT LOCATION
leap15.2             480.1      x86_64     /opt/ohpc/admin/images/leap15.2

 

도움말 모음

> wwsh help
Warewulf command line shell interface

Welcome to the Warewulf shell interface. This application allows you
to interact with the Warewulf backend database and modules via a
single interface.

  bootstrap        Manage your bootstrap images
  dhcp             Manage DHCP service and configuration
  events           Control how events are handled
  exit             Exit/leave the Warewulf shell
  file             Manage files within the Warewulf data store
  ipmi             Node IPMI configuration
  node             Node manipulation commands
  object           Generically manipulate all Warewulf data store entries
  output           Set the output verbosity level
  provision        Node provision manipulation commands
  pxe              Manage PXE configuration
  quit             Exit/leave the Warewulf shell
  ssh              Spawn parallel ssh connections to nodes.
  vnfs             Manage your VNFS images
USAGE:
     bootstrap <command> [options] [targets]

SUMMARY:
     This interface allows you to manage your bootstrap images within the Warewulf
     data store.

COMMANDS:

         import          Import a bootstrap image into Warewulf
         export          Export a bootstrap image to the local file system
         delete          Delete a bootstrap image from Warewulf
         list            Show all of the currently imported bootstrap images
         set             Set bootstrap attributes
         (re)build       Build (or rebuild) the tftp bootable image(s) on this host
         help            Show usage information
USAGE:
     dhcp <command>

SUMMARY:
        The DHCP command configures/reconfigures the DHCP service.

COMMANDS:

         update          Update the DHCP configuration, and restart the service
         restart         Restart the DHCP service
         help            Show usage information
USAGE:
     events [command]

SUMMARY:
     Control how/if events are handled.

COMMANDS:

     enable          Enable all events for this shell (default)
     disable         Disable the event handler
     help            Show usage information
USAGE:
     file <command> [options] [targets]

SUMMARY:
     The file command is used for manipulating file objects.  It allows you to
     import, export, create, and modify files within the Warewulf data store.
     File objects may be used to supply files to nodes at provision time,
     dynamically create files or scripts based on Warewulf data and more.

COMMANDS:
     import             Import a file into a file object
     export             Export file object(s)
     edit               Edit the file in the data store directly
     new                Create a new file in the data store
     set                Set file attributes/metadata
     show               Show the contents of a file
     list               List a summary of imported file(s)
     print              Print all file attributes
     (re)sync           Sync the data of a file object with its source(s)
     delete             Remove a node configuration from the data store
     help               Show usage information
USAGE:
     ipmi <command> [options] [targets]

SUMMARY:
    The ipmi command is used for setting node ipmi configuration attributes.

COMMANDS:

         set             Modify an existing node configuration
         list            List a summary of the node(s) ipmi configuration
         print           Print the full node(s) ipmi configuration
         poweron         Power on the list of nodes
         poweroff        Power off the list of nodes
         powercycle      Power cycle the list of nodes
         powerstatus     Print the power status of the nodes
         ident           Set chassis identify light to on for the nodes
         noident         Set chassis identify light to off for the nodes
         printsel        Print system event log for the nodes
         clearsel        Clear system event log for the nodes
         printsdr        Print sensor data records for the nodes
         console         Start a serial-over-lan console session.
                         NOTE: Requires that a serial console be defined
                         in kernel arguments, i.e. console=ttyS0,57600

         forcepxe        Force next boot from PXE
         forcedisk       Force next boot from first Hard Drive
         forcecdrom      Force next boot from CD-ROM
         forcebios       Force next boot into BIOS

         help            Show usage information
USAGE:
     node <command> [options] [targets]

SUMMARY:
     The node command is used for viewing and manipulating node objects.

COMMANDS:
         new             Create a new node configuration
         set             Modify an existing node configuration
         list            List a summary of nodes
         print           Print the node configuration
         delete          Remove a node configuration from the data store
         clone           Clone a node configuration to another node
         help            Show usage information
USAGE:
     object <command> [options] [targets]

SUMMARY:
     The object command provides an interface for generically manipulating all
     object types within the Warewulf data store.

COMMANDS:
     modify          Add, delete, and/or set object member variables
     print           Display object(s) and their members
     delete          Completely remove object(s) from the data store
     dump            Recursively dump objects in internal format
     jsondump        Recursively dump objects in json format
     canonicalize    Check and update objects to current standard format
     help            Show usage information
USAGE:
     output [command]

SUMMARY:
    This command sets the desired command output verbosity level.

COMMANDS:

         normal          The standard (and default) output level intended for
                         normal usage
         quiet           Only print warning, error, or critical messages.
         verbose         Increase verbosity over the normal output level
         debug           Show debugging messages (very verbose)
USAGE:
     provision <command> [options] [targets]

SUMMARY:
    The provision command is used for setting node provisioning attributes.

COMMANDS:

         set             Modify an existing node configuration
         list            List a summary of the node(s) provision configuration
         print           Print the full node(s) provision configuration
         help            Show usage information
USAGE:
     pxe <command> [options] [targets]

SUMMARY:
        Manage PXE configuration.

COMMANDS:

         update          Update the PXE configuration
         help            Show usage information
USAGE:
     ssh [nodes/targets] [command]

SUMMARY:
     Run ssh connections to node(s) in parallel by either node names, group
     or any other known lookup.
USAGE:
     vnfs <command> [options] [targets]

SUMMARY:
     This interface allows you to manage your VNFS images within the Warewulf
     data store.

COMMANDS:

         import          Import a VNFS image into Warewulf
         export          Export a VNFS image to the local file system
         delete          Delete a VNFS image from Warewulf
         list            Show all of the currently imported VNFS images
         set             Set any VNFS attributes
         help            Show usage information

 

 

 

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프로그램 사용/rtl-sdr2021. 1. 5. 19:45

rtl sdr driver libusb

rtl-sdr을 쓰지 않고 원본 드라이버로 먼가하고 싶긴한데 영 안되네..

rtl-sdr

libusb

librtlsdr

여러가지가 보이는데 서로 연관이 어떻게 되는건가..

 

[링크 : http://blog.tcltk.co.kr/?p=888]

[링크 : http://github.com/steve-m/librtlsdr/blob/master/src/librtlsdr.c]

 

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